import numpy as np
import pandas as pd
from itertools import cycle
from scipy import interp
# sklearn
from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
# TensorFlow
import tensorflow as tf
# Visualisation libraries
## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output
## progressbar
import progressbar
## plotly
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
## seaborn
import seaborn as sns
## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
from matplotlib import cm
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

In this article, we demonstrate implementing the Tensorflow Linear classifier model by an example. The details regarding this dataset can be found in the Diagnostic Wisconsin Breast Cancer Database.
data = datasets.load_breast_cancer()
Data = pd.DataFrame(data['data'], columns = [x.title() for x in data['feature_names']])
Labels_dict = dict(zip(list(np.sort(np.unique(data['target'].tolist()))),
list([x.title() for x in data['target_names']])))
Target = 'Diagnosis'
Data[Target] = data['target']
# Data['Diagnosis'] = data['target'].replace(Labels_dict)
display(Data)
print(data['DESCR'])
| Mean Radius | Mean Texture | Mean Perimeter | Mean Area | Mean Smoothness | Mean Compactness | Mean Concavity | Mean Concave Points | Mean Symmetry | Mean Fractal Dimension | ... | Worst Texture | Worst Perimeter | Worst Area | Worst Smoothness | Worst Compactness | Worst Concavity | Worst Concave Points | Worst Symmetry | Worst Fractal Dimension | Diagnosis | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 17.99 | 10.38 | 122.80 | 1001.0 | 0.11840 | 0.27760 | 0.30010 | 0.14710 | 0.2419 | 0.07871 | ... | 17.33 | 184.60 | 2019.0 | 0.16220 | 0.66560 | 0.7119 | 0.2654 | 0.4601 | 0.11890 | 0 |
| 1 | 20.57 | 17.77 | 132.90 | 1326.0 | 0.08474 | 0.07864 | 0.08690 | 0.07017 | 0.1812 | 0.05667 | ... | 23.41 | 158.80 | 1956.0 | 0.12380 | 0.18660 | 0.2416 | 0.1860 | 0.2750 | 0.08902 | 0 |
| 2 | 19.69 | 21.25 | 130.00 | 1203.0 | 0.10960 | 0.15990 | 0.19740 | 0.12790 | 0.2069 | 0.05999 | ... | 25.53 | 152.50 | 1709.0 | 0.14440 | 0.42450 | 0.4504 | 0.2430 | 0.3613 | 0.08758 | 0 |
| 3 | 11.42 | 20.38 | 77.58 | 386.1 | 0.14250 | 0.28390 | 0.24140 | 0.10520 | 0.2597 | 0.09744 | ... | 26.50 | 98.87 | 567.7 | 0.20980 | 0.86630 | 0.6869 | 0.2575 | 0.6638 | 0.17300 | 0 |
| 4 | 20.29 | 14.34 | 135.10 | 1297.0 | 0.10030 | 0.13280 | 0.19800 | 0.10430 | 0.1809 | 0.05883 | ... | 16.67 | 152.20 | 1575.0 | 0.13740 | 0.20500 | 0.4000 | 0.1625 | 0.2364 | 0.07678 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 564 | 21.56 | 22.39 | 142.00 | 1479.0 | 0.11100 | 0.11590 | 0.24390 | 0.13890 | 0.1726 | 0.05623 | ... | 26.40 | 166.10 | 2027.0 | 0.14100 | 0.21130 | 0.4107 | 0.2216 | 0.2060 | 0.07115 | 0 |
| 565 | 20.13 | 28.25 | 131.20 | 1261.0 | 0.09780 | 0.10340 | 0.14400 | 0.09791 | 0.1752 | 0.05533 | ... | 38.25 | 155.00 | 1731.0 | 0.11660 | 0.19220 | 0.3215 | 0.1628 | 0.2572 | 0.06637 | 0 |
| 566 | 16.60 | 28.08 | 108.30 | 858.1 | 0.08455 | 0.10230 | 0.09251 | 0.05302 | 0.1590 | 0.05648 | ... | 34.12 | 126.70 | 1124.0 | 0.11390 | 0.30940 | 0.3403 | 0.1418 | 0.2218 | 0.07820 | 0 |
| 567 | 20.60 | 29.33 | 140.10 | 1265.0 | 0.11780 | 0.27700 | 0.35140 | 0.15200 | 0.2397 | 0.07016 | ... | 39.42 | 184.60 | 1821.0 | 0.16500 | 0.86810 | 0.9387 | 0.2650 | 0.4087 | 0.12400 | 0 |
| 568 | 7.76 | 24.54 | 47.92 | 181.0 | 0.05263 | 0.04362 | 0.00000 | 0.00000 | 0.1587 | 0.05884 | ... | 30.37 | 59.16 | 268.6 | 0.08996 | 0.06444 | 0.0000 | 0.0000 | 0.2871 | 0.07039 | 1 |
569 rows × 31 columns
.. _breast_cancer_dataset:
Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------
**Data Set Characteristics:**
:Number of Instances: 569
:Number of Attributes: 30 numeric, predictive attributes and the class
:Attribute Information:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.
- class:
- WDBC-Malignant
- WDBC-Benign
:Summary Statistics:
===================================== ====== ======
Min Max
===================================== ====== ======
radius (mean): 6.981 28.11
texture (mean): 9.71 39.28
perimeter (mean): 43.79 188.5
area (mean): 143.5 2501.0
smoothness (mean): 0.053 0.163
compactness (mean): 0.019 0.345
concavity (mean): 0.0 0.427
concave points (mean): 0.0 0.201
symmetry (mean): 0.106 0.304
fractal dimension (mean): 0.05 0.097
radius (standard error): 0.112 2.873
texture (standard error): 0.36 4.885
perimeter (standard error): 0.757 21.98
area (standard error): 6.802 542.2
smoothness (standard error): 0.002 0.031
compactness (standard error): 0.002 0.135
concavity (standard error): 0.0 0.396
concave points (standard error): 0.0 0.053
symmetry (standard error): 0.008 0.079
fractal dimension (standard error): 0.001 0.03
radius (worst): 7.93 36.04
texture (worst): 12.02 49.54
perimeter (worst): 50.41 251.2
area (worst): 185.2 4254.0
smoothness (worst): 0.071 0.223
compactness (worst): 0.027 1.058
concavity (worst): 0.0 1.252
concave points (worst): 0.0 0.291
symmetry (worst): 0.156 0.664
fractal dimension (worst): 0.055 0.208
===================================== ====== ======
:Missing Attribute Values: None
:Class Distribution: 212 - Malignant, 357 - Benign
:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
:Donor: Nick Street
:Date: November, 1995
This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2
Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image.
Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.
The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
.. topic:: References
- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
San Jose, CA, 1993.
- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
July-August 1995.
- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
163-171.
As can be seen, the number of instances is 569 and the number of attributes is 32. The object of the exercise is to create a classification model that can classify the type of Diagnosis base on the rest of the attributes. However, first, let's plot a count plot for Diagnosis attribute.
X, y = data.data, data.target
Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.
X = pd.DataFrame(data = X, columns = [x.title() for x in data['feature_names']])
# scaling data
scaler = preprocessing.StandardScaler()
X_std = scaler.fit_transform(X)
X_std = pd.DataFrame(data = X_std, columns = [x.title() for x in data['feature_names']])
del scaler
fig, ax = plt.subplots(2, 1, figsize=(20, 8))
ax = ax.ravel()
font = FontProperties()
font.set_weight('bold')
CP = [sns.color_palette("OrRd", 20), sns.color_palette("Greens", 20)]
Names = ['Variance of the Features', 'Variance of the Features (Standardized)']
Sets = [X, X_std]
kws = dict(label='Feature\nVariance', aspect=20, shrink= .3)
for i in range(len(ax)):
Temp = Sets[i].var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T
_ = sns.heatmap(Temp, ax=ax[i], annot=True, square=True, cmap = CP[i],
linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0], annot_kws={"size": 8},
cbar_kws=kws)
_ = ax[i].set_yticklabels('')
_ = ax[i].set_title(Names[i], fontproperties=font, fontsize = 16)
del Temp
X = X_std.copy()
del CP, Names, ax, fig, font, Sets, kws, X_std
def DatasetTargetDist(Inp, Target, Labels_dict, PD):
# Table
Table = Inp[Target].value_counts().to_frame('Count').reset_index(drop = False).rename(columns = {'index':Target})
Table[Target] = Table[Target].replace(Labels_dict)
Table['Percentage'] = np.round(100*(Table['Count']/Table['Count'].sum()),2)
fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=PD['column_widths'],
specs=[[{"type": "table"},{"type": "pie"}]])
# Right
fig.add_trace(go.Pie(labels=Table[Target].values, values=Table['Count'].values,
pull=PD['pull'], textfont=dict(size= PD['textfont']),
marker=dict(colors = PD['PieColors'], line=dict(color='black', width=1))), row=1, col=2)
fig.update_traces(hole=PD['hole'])
fig.update_layout(height = PD['height'], legend=dict(orientation="v"), legend_title_text= PD['legend_title'])
# Left
T = Table.copy()
T['Percentage'] = T['Percentage'].map(lambda x: '%%%.2f' % x)
Temp = []
for i in T.columns:
Temp.append(T.loc[:,i].values)
fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
fill_color= PD['TableColors'][0], align=['center','center'],
font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
cells=dict(values=Temp, line_color='darkslategray',
fill=dict(color= [PD['TableColors'][1], PD['TableColors'][1]]),
align=['center', 'center'], font_size=12, height=20)), 1, 1)
fig.update_layout(title={'text': '<b>' + Target + '<b>', 'x':PD['title_x'],
'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
fig.show()
Pull = [0 for x in range((len(Labels_dict)-1))]
Pull.append(.05)
PD = dict(PieColors = ['SeaGreen','FireBrick'],
TableColors = ['Navy','White'], hole = .4,
column_widths=[0.6, 0.4],textfont = 14, height = 350, tablecolumnwidth = [0.20, 0.12, 0.15],
pull = Pull, legend_title = Target, title_x = 0.5, title_y = 0.8)
del Pull
DatasetTargetDist(Data, Target, Labels_dict, PD)
StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.
Test_Size = 0.3
sss = StratifiedShuffleSplit(n_splits=1, test_size=Test_Size, random_state=42)
_ = sss.get_n_splits(X, y)
for train_index, test_index in sss.split(X, y):
# X
if isinstance(X, pd.DataFrame):
X_train, X_test = X.loc[train_index], X.loc[test_index]
else:
X_train, X_test = X[train_index], X[test_index]
# y
if isinstance(y, pd.Series):
y_train, y_test = y[train_index], y[test_index]
else:
y_train, y_test = y[train_index], y[test_index]
del sss
def Train_Test_Dist(X_train, y_train, X_test, y_test, PD, Labels_dict = Labels_dict):
def ToSeries(x):
if not isinstance(x, pd.Series):
Out = pd.Series(x)
else:
Out = x.copy()
return Out
fig = make_subplots(rows=1, cols=3, horizontal_spacing = 0.02, column_widths= PD['column_widths'],
specs=[[{"type": "table"},{'type':'domain'}, {'type':'domain'}]])
# Right
C = 2
for y in [ToSeries(y_train).replace(Labels_dict), ToSeries(y_test).replace(Labels_dict)]:
fig.add_trace(go.Pie(labels= list(Labels_dict.values()),
values= y.value_counts().values, pull=PD['pull'],
textfont=dict(size=PD['textfont']),
marker=dict(colors = PD['PieColors'],
line=dict(color='black', width=1))), row=1, col=C)
fig.update_traces(hole=.5)
fig.update_layout(legend=dict(orientation="v"), legend_title_text= PD['legend_title'])
C+=1
# Left
# Table
Table = pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).astype(str)
T = Table.copy()
Temp = []
for i in T.columns:
Temp.append(T.loc[:,i].values)
TableColors = PD['TableColors']
fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
fill_color= TableColors[0], align=['center','center'],
font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
cells=dict(values=Temp, line_color='darkslategray',
fill=dict(color= [TableColors[1], TableColors[1]]),
align=['center', 'center'], font_size=12, height=20)), 1, 1)
fig.update_layout(title={'text': '<b>' + 'Dataset Distribution' + '<b>', 'x':PD['title_x'],
'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
if not PD['height'] == None:
fig.update_layout(height = PD['height'])
fig.show()
PD.update(dict(PieColors = ['FireBrick', 'SeaGreen'], column_widths=[0.3, 0.3, 0.3],
tablecolumnwidth = [0.2, 0.4], height = 350, legend_title = Target))
Train_Test_Dist(X_train, y_train, X_test, y_test, PD)
Here, we use the Tensorflow Linear classifier model.tf.estimator.LinearClassifier.
The input function specifies how data is converted to a tf.data.Dataset that feeds the input pipeline in a streaming fashion. Moreover, an input function is a function that returns a tf.data.Dataset object which outputs the following two-element tuple:
def input_fn(features, labels, training=True, batch_size=256):
"""An input function for training or evaluating"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()
return dataset.batch(batch_size)
Moreover, an estimator model consists of two main parts, feature columns, and a numeric vector. Feature columns provide explanations for the input numeric vector. The following function separates categorical and numerical columns (features)and returns a descriptive list of feature columns.
def Feat_Columns(Inp):
Temp = Inp.dtypes.reset_index(drop = False)
Temp.columns = ['Features', 'Data Type']
Temp['Data Type'] = Temp['Data Type'].astype(str)
# Numeric_Columns
Numeric_Columns = Temp.loc[Temp['Data Type'].isin(['int64', 'int32', 'float64', 'float32']),'Features'].tolist()
# Categorical_Columns
Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
# Feature Columns
feature_columns = []
if len(Categorical_Columns)>0:
for feature_name in Categorical_Columns:
vocabulary = Inp[feature_name].unique()
feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
if len(Numeric_Columns)>0:
for feature_name in Numeric_Columns:
feature_columns.append(tf.feature_column.numeric_column(feature_name))
return feature_columns
my_feature_columns = Feat_Columns(X)
tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns)
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
| accuracy | accuracy_baseline | auc | auc_precision_recall | average_loss | label/mean | loss | precision | prediction/mean | recall | global_step | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9766 | 0.6257 | 0.9974 | 0.9984 | 0.0586 | 0.6257 | 0.0586 | 0.9725 | 0.6233 | 0.9907 | 5000 |
def ROC_Curve(y_test, probs, n_classes, FS = 7, ax = False, pad = 0.01):
# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes=n_classes, dtype='float32')
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = metrics.roc_curve(y_test_cat[:, i], probs[:, i])
roc_auc[i] = metrics.auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = metrics.roc_curve(y_test_cat.ravel(), probs.ravel())
roc_auc["micro"] = metrics.auc(fpr["micro"], tpr["micro"])
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = metrics.auc(fpr["macro"], tpr["macro"])
fig = go.Figure()
fig.add_trace(go.Scatter(x=[0, 1], y=[0, 1], name = 'FPR = TPR', line = dict(color='Black', width=2, dash='dash')))
fig.add_trace(go.Scatter(x=fpr["micro"], y=tpr["micro"], mode='lines', marker_color = 'deeppink',
name='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"])))
fig.add_trace(go.Scatter(x=fpr["macro"], y=tpr["macro"], mode='lines', marker_color = 'navy',
name='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"])))
colors = cycle(['Aqua', 'DarkOrange', 'CornflowerBlue'])
for i, color in zip(range(n_classes), colors):
_ = fig.add_trace(go.Scatter(x = fpr[i], y = tpr[i], mode='lines', marker_color= px.colors.sequential.Rainbow,
name='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i])))
# Background
fig.update_layout(plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
zeroline=True, zerolinewidth=1, zerolinecolor='Black',
showgrid=True, gridwidth=1, gridcolor='Lightgray', range =[-pad, 1+pad],
title = 'False Positive Rate (FPR)')
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
zeroline=True, zerolinewidth=1, zerolinecolor='Black',
showgrid=True, gridwidth=1, gridcolor='Lightgray', range =[-pad, 1+pad],
title = 'True Positive Rate (TPR)')
fig.update_yaxes(scaleanchor = "x", scaleratio = 1)
fig.update_layout(height = 600, width = 810)
fig.update_layout(title={'text': '<b>' + 'Receiver Operating Characteristic (ROC) Curves' + '<b>', 'x': .5,
'y': .9, 'xanchor': 'center', 'yanchor': 'top'})
fig.show()
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
ROC_Curve(y_test, probs, n_classes = len(Labels_dict), FS = 8)
The confusion matrix allows for visualization of the performance of an algorithm. Note that due to the size of data, here we don't provide a Cross-validation evaluation. In general, this type of evaluation is preferred.
def Confusion_Mat(CM_Train, CM_Test, PD, n_splits = 10):
if n_splits == None:
Titles = ['Train Set', 'Test Set']
else:
Titles = ['Train Set (CV = % i)' % n_splits, 'Test Set (CV = % i)' % n_splits]
CM = [CM_Train, CM_Test]
Cmap = ['Greens', 'YlGn','Blues', 'PuBu']
for i in range(2):
fig, ax = plt.subplots(1, 2, figsize= PD['FS'])
fig.suptitle(Titles[i], weight = 'bold', fontsize = 16)
_ = sns.heatmap(CM[i], annot=True, annot_kws={"size": PD['annot_kws']}, cmap=Cmap[2*i], ax = ax[0],
linewidths = 0.2, cbar_kws={"shrink": PD['shrink']})
_ = ax[0].set_title('Confusion Matrix');
Temp = np.round(CM[i].astype('float') / CM[i].sum(axis=1)[:, np.newaxis], 2)
_ = sns.heatmap(Temp,
annot=True, annot_kws={"size": PD['annot_kws']}, cmap=Cmap[2*i+1], ax = ax[1],
linewidths = 0.4, vmin=0, vmax=1, cbar_kws={"shrink": PD['shrink']})
_ = ax[1].set_title('Normalized Confusion Matrix');
for a in ax:
_ = a.set_xlabel('Predicted labels')
_ = a.set_ylabel('True labels');
_ = a.xaxis.set_ticklabels(PD['Labels'])
_ = a.yaxis.set_ticklabels(PD['Labels'])
_ = a.set_aspect(1)
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)
Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
display(Reports_Train.style.hide_index().set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide_index().set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))
PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
| Train Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 0.993151 | 0.979730 | 0.986395 | 148.000000 |
| Benign | 0.988095 | 0.996000 | 0.992032 | 250.000000 |
| accuracy | 0.989950 | 0.989950 | 0.989950 | 0.989950 |
| macro avg | 0.990623 | 0.987865 | 0.989213 | 398.000000 |
| weighted avg | 0.989975 | 0.989950 | 0.989936 | 398.000000 |
| Test Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 0.983871 | 0.953125 | 0.968254 | 64.000000 |
| Benign | 0.972477 | 0.990654 | 0.981481 | 107.000000 |
| accuracy | 0.976608 | 0.976608 | 0.976608 | 0.976608 |
| macro avg | 0.978174 | 0.971890 | 0.974868 | 171.000000 |
| weighted avg | 0.976741 | 0.976608 | 0.976531 | 171.000000 |
The Follow the Regularized Leader (FTRL) model is an implementation of the FTRL-Proximal online learning algorithm for binomial logistic regression (for details see [6]).
tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns,
optimizer=tf.keras.optimizers.Ftrl(learning_rate=0.1, l1_regularization_strength=0.001))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
| accuracy | accuracy_baseline | auc | auc_precision_recall | average_loss | label/mean | loss | precision | prediction/mean | recall | global_step | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9825 | 0.6257 | 0.9978 | 0.9986 | 0.0553 | 0.6257 | 0.0553 | 0.9906 | 0.6166 | 0.9813 | 5000 |
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
ROC_Curve(y_test, probs, n_classes = len(Labels_dict), FS = 8)
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)
Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
display(Reports_Train.style.hide_index().set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide_index().set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))
PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
| Train Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 0.993151 | 0.979730 | 0.986395 | 148.000000 |
| Benign | 0.988095 | 0.996000 | 0.992032 | 250.000000 |
| accuracy | 0.989950 | 0.989950 | 0.989950 | 0.989950 |
| macro avg | 0.990623 | 0.987865 | 0.989213 | 398.000000 |
| weighted avg | 0.989975 | 0.989950 | 0.989936 | 398.000000 |
| Test Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 0.969231 | 0.984375 | 0.976744 | 64.000000 |
| Benign | 0.990566 | 0.981308 | 0.985915 | 107.000000 |
| accuracy | 0.982456 | 0.982456 | 0.982456 | 0.982456 |
| macro avg | 0.979898 | 0.982842 | 0.981330 | 171.000000 |
| weighted avg | 0.982581 | 0.982456 | 0.982483 | 171.000000 |
tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns,
optimizer=lambda: tf.keras.optimizers.Adam(learning_rate=tf.compat.v1.train.exponential_decay(learning_rate=0.1,
global_step=tf.compat.v1.train.get_global_step(), decay_steps=IT,decay_rate=0.96)))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
| accuracy | accuracy_baseline | auc | auc_precision_recall | average_loss | label/mean | loss | precision | prediction/mean | recall | global_step | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9532 | 0.6257 | 0.9554 | 0.9621 | 0.4517 | 0.6257 | 0.4517 | 0.9626 | 0.6172 | 0.9626 | 5000 |
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
ROC_Curve(y_test, probs, n_classes = len(Labels_dict), FS = 8)
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)
Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
display(Reports_Train.style.hide_index().set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide_index().set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))
PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
| Train Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 1.000000 | 1.000000 | 1.000000 | 148.000000 |
| Benign | 1.000000 | 1.000000 | 1.000000 | 250.000000 |
| accuracy | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| macro avg | 1.000000 | 1.000000 | 1.000000 | 398.000000 |
| weighted avg | 1.000000 | 1.000000 | 1.000000 | 398.000000 |
| Test Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Malignant | 0.937500 | 0.937500 | 0.937500 | 64.000000 |
| Benign | 0.962617 | 0.962617 | 0.962617 | 107.000000 |
| accuracy | 0.953216 | 0.953216 | 0.953216 | 0.953216 |
| macro avg | 0.950058 | 0.950058 | 0.950058 | 171.000000 |
| weighted avg | 0.953216 | 0.953216 | 0.953216 | 171.000000 |
W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.
O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.
W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.